Understanding AlphaFold Metrics in Structure Evaluation
AlphaFold is a deep learning model developed by the DeepMind team at Google for predicting the three-dimensional structure of proteins. In case, you are new to AlphaFold, here is a quick introduction to the program. AlphaFold uses a neural network to predict the three-dimensional structure of a protein from its amino acid sequence. The neural network is trained on a large dataset of known protein structures and amino acid sequences, using a technique called supervised learning. The model uses a combination of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to learn the complex relationships between protein sequences and structures. The output of the AlphaFold model is a prediction of the three-dimensional structure of the protein, represented as a set of coordinates for each atom in the protein. These predictions are evaluated using a range of metrics, including the predicted local distance difference test (PLDDT), predicted torsion angle metri...